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The Generative AI Con

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321–330 of 503 posts

Re: The Generative AI Con

#321
post #309

ChatGPT and LLMs have had a significant impact on my wife's life. She's a second language speaker, and having ChatGPT available to draft and proofread professional sounding emails and text messages has drastically increased her self-confidence and ability to communicate with colleagues. I think that's amazing. That's also the only use of LLMs we've found.

The downside to doing this is that you'll sound like an LLM. LLM-generated text is very obvious to anyone with basic reading comprehension and once detected will cause some people to summarily dismiss the sender as a bot.

This is more than acceptable if it allows you to confidently send of an email in less than a minute that would otherwise take you 30 minutes of agony to write and still not be confident about.

Also, these aren't cold calls. The recipients aren't critical about how "botty" the email sounds.

Re: The Generative AI Con

#322
post #220

Protein folding is a an application of generative AI that will probably produce trillions of dollars of value in the long term. It was probably impossible for Google to squeeze that sort of money from researchers who use it, but it proves that the technology definitely useful. Another application that is highly underrated is with robots completing complicated tasks.

How is predicting protein folding GenAI? Seems like traditional machine learning?

I suppose I'm thinking about transformer architecture rather than strictly GenAI, but the computer science aspects of protein folding and GenAI seem like they overlap significantly.

Re: The Generative AI Con

#323
post #107
post #12

I’m a little shocked at how much negativity there is around LLMs among developers. It’s a new tool that requires some learning, and it’s sometimes not so great, but if you’ve used an IDE with real coding assistance built in (eg. VS Code in Edit with Copilot mode - NOT Chat mode, using Claude 3.5), it’s honestly not much worse than a junior dev and 100x faster. And if the code is bad you throw it away and try again 10…

> I’m a little shocked at how much negativity there is around LLMs among developers. While the timeline is unclear; it seems likely that LLMs will obsolete precisely the skills that developers use to earn their income. I imagine a lot of them feel rather threatened by the rapid rate of progress. Pointing out that it is already operating at junior dev quality and rapidly improving is unlikely to quiet the discontent.

> it seems likely that LLMs will obsolete precisely the skills that developers use to earn their income

I’m not particularly worried. I think it’s obvious that software engineering is definitely an “intelligence complete” problem. Any system that can do software engineering can solve any problem that requires intelligence. So, either my job is safe or I get to live through the fall of almost all white collar disciplines. There’s not a huge middle ground.

Although perhaps this is just the programmer stereotype of thinking that if someone can code, they can do anything.

Re: The Generative AI Con

#324
post #303

Earlier quoted context omitted.

Seems to be the case to me, reading this and waymo's attempts. There's paper on EMMA here - https://arxiv.org/abs/2410.23262 And there are state of the art weather prediction transformers. https://arxiv.org/abs/2312.03876

Yeah so like, this is a cool result, and it uses a transformer architecture. I actually do think that it's fair to say that transformers have proven widely useful, especially in tasks that look like sequence modeling. It's a step change akin to the now-pervasive use of convolutional neural networks that started in the 2010s, and is deeply significant of course. This is also really different from "this is an LLM" The…

What you're saying does happen to some degree and in this instance, if i had linked some advance with a diffusion model then i would get it but about the only difference between this and chatgpt is the data it's been trained on. If Open AI cared, the next version of GPT could be a State of the Art weather predictor.

Re: The Generative AI Con

#325
post #38

Earlier quoted context omitted.

https://news.ycombinator.com/item?id=43071381 You can see this example where I timed myself to deployment using AI tools to rewrite a show HN project in half an hour. The code is open source. My comment was posted 2 hours after show HN when I saw it on front page so you know I didn’t lose track of time I spent.

The vast majority of software work is not greenfielding a PoC or reimplementing an existing, small, well-specced project. We’ve had OpenAPI client generators for years after all. The majority of software work is maintaining large, existing products: adding features, fixing bugs, improving performance, etc., or building new software in problem domains that aren’t so well-defined.

This is my experience too.

I think it also really accelerates learning of a new language or framework, when that language or framework is really well documented on the web. For novel programming frameworks, obviously it's a bit more challenging to get help from an LLM.

One of more recent attempts at using LLM code assist was to try to fix a bug in a Swift SSH Agent's connection handling that was causing hangs. I know zero Swift, much less the networking frameworks. So I pumped the output of `tree` on the git repo into the LLM, asked for which file likely handled connections, and it found it right away. That's probably 15 minutes saved. Before putting in the file I asked for likely reasons for deadhangs, got that list, then put in the Swift file that handled connections, and it pointed to what the likely problem was. That's probably 1 hour+ of reading documentation to try to figure out what the code was doing wrong with the networking framework, assuming the LLM was not hallucinating. And that "not hallucinating" probability is high enough in my experience that I spend >50% of my time trying to verify I'm not getting bullshitted.

The LLM proposed a fix (~10-20 minute savings), but even as somebody who doesn't use Swift it seemed like >99% chance that it had just introduced a bunch of race conditions in the data structures it used to track connection status. So I asked about it, and it said "Oh yeah of course how could I forget" and then significantly complicated the solution with something that I thought looked like it probably worked. But was the LLM just being obsequious or was it correct the first time? So hard to tell...

So in about 20 minutes I probably accomplished in a language I didn't know, in a code base I didn't know, about 2 hours+ of learning.

But if I knew the language, it would have saved me very little time, and may have cost me some time.

Re: The Generative AI Con

#326
post #91

If my Android (or IPhone) disappeared tomorrow, I would feel like I time traveled back a century. If Google search was gone, I wouldn't be able to do my job anymore. If the cloud disappeared, I wouldn't be able to build apps anymore. There are no workarounds, unless you feel like going to a library...? If ChatGPT disappeared tomorrow (or derivatives like Copilot, etc.), I would be mildly inconvenienced. Then I'd go b…

[deleted]

Re: The Generative AI Con

#327
post #38

Earlier quoted context omitted.

I keep on trying them, but if they are useful they are useful for only a small fraction of engineers at the moment. I'm not sure if this is due to the nature of the work, or the nature of the user. I have heard "top" engineers at various places say it makes them 2x faster, or whatever, but I would like to see this assessed by timed testing, as is sometimes done for evaluating software engineering. Copilot may let me…

https://news.ycombinator.com/item?id=43071381 You can see this example where I timed myself to deployment using AI tools to rewrite a show HN project in half an hour. The code is open source. My comment was posted 2 hours after show HN when I saw it on front page so you know I didn’t lose track of time I spent.

I agree it's impressive and stuff, but I wouldn't consider a JS POC as a serious project. I have never done that in my whole life and would rather see results from a 10 years old application with a million lines of code of C++. That's would be realistic. What you did is refactoring a pet project and I don't know why we're wasting $billions for that.

Re: The Generative AI Con

#328
post #244

Earlier quoted context omitted.

>> If you think LLMs operate at "junior dev" capacity you either don't work with junior devs and is just bullshitting your way around here, or you just pick pretty awful junior devs. I’ve hired lots of junior devs, some of them very capable. I’ve been in this industry for more than 15 years. LLMs operate at junior dev capacity, that’s pretty clear to me at this moment.

I sincerely doubt both your experience and your ability to hire decent devs.

I know, it's an highly unpopular opinion among devs. Let's revisit this comment in 5 years...

Re: The Generative AI Con

#329
post #10

Earlier quoted context omitted.

> The valuations seem to be primarily based on the R&D progress There hasn't been much R&D progress, though. Sure, as another commenter pointed out, context lengths have gotten longer and chat models can interpret images now, but the industry figureheads have been pushing agents, and we're not much closer to those than we were two years ago when GPT-4 came out. Current models simply are not consistent enough to do th…

Recent results are showing exponential improvement in reasoning and dramatic decreases in the time and cost to train models. O3 now ranks 50th on code forces according to openai staff. Are you aware of all of this and still say R&D hasn’t progressed?

In the context of Moore's law exponential growth was measured in the number of transistors per integrated circuit. This seems vigorous and straightforward.

With AI the improvements have certainly been impressive but it isn't straightforward how you can define "reasoning" to measure whether or not the reasoning is exponentially "improving".

Re: The Generative AI Con

#330

This is one of the most tilted pieces I’ve ever read. For months Ed has predicted The AI Bubble will burst “any day now” frequently citing ai company’s revenue as a sign the product is not viable and the valuations are too high. The valuations seem to be primarily based on the R&D progress instead of on a theory that widespread adoption of the existing product will experience an uptick. The current landscape imho sho…

I can’t help but be reminded of Greenspan’s remarks on the housing market in 2006 while reading this comment: While he was chairman of the central bank through January 2006, Greenspan always denied there was a bubble in the nationwide U.S. real estate market, saying only that a certain number of metropolitan real estate markets could see declines in home values because of a localized run-up in prices. That view of an…

A failure of imagination I suppose.
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